Distribution of Earthquake Interevent Times in Northeast India and Adjoining Regions

Geomatics, Natural Hazards and Risk

2018

Self Cite

This work concentrates on the distribution of earthquake interevent times in northwest Himalaya and its adjacent regions. We consider 12 timedependent probability distributions for analysis. The maximum likelihood estimation and Fisher information matrix-based methods are used to estimate model parameters and their respective uncertainties. Results from three model selection criteria suggest that the best fit arises from exponentiated Weibull, exponentiated exponential, Weibull and gamma distributions. An intermediate fit comes from exponentiated Rayleigh, and lognormal distributions, whereas the remaining distributions exhibit a poor fit to the seismic interoccurrence times of present catalogue. Using exponentiated Weibull model, it is observed that the estimated cumulative probability of magnitude 6.0 or higher event in the northwest Himalaya reaches 0.92-0.95 after about 20-23 (2019-2022) years since the last event in 1999. The conditional probability, for an elapsed time of 19 years (i.e. 2018), reaches 0.90-0.95 after about 20-25 (2038-2043) years from now. A series of conditional probability curves is also presented to understand the recent and future earthquake hazard in the study region. This temporal evolution of seismic interevent times may provide important clues to the underlying physical mechanism of earthquake genesis in the northwest Himalaya region.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic modelling of earthquake interoccurrence times in Northwest Himalaya and adjoining regions

Geomatics, Natural Hazards and Risk

2018

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“…Later, several researchers (e.g., Parvez and Ram 1997;Tripathi 2006;Yadav et al 2008Yadav et al , 2010Yazdani and Kowsari 2011;Chen et al 2013;Pasari and Dikshit 2014a;Pasari 2015) applied similar methodologies for their respective geographic regions of study. The suitability of other probability models such as the Gaussian distribution (Papazachos et al 1987), the negative binomial distribution (Dionysiou and Papadopoulos 1992), the Levy distribution (Sotolongo-Costa et al 2000;Pasari and Dikshit 2014b), the Pareto group of distributions (Kagan and Schoenberg 2001;Ferraes 2003), the generalized gamma distribution (Bak et al 2002), the Brownian passage time distribution (Matthews et al 2002;Pasari and Dikshit 2014b), the Rayleigh distribution (Ferraes 2003;Yazdani and Kowsari 2011), the inverse Weibull distribution (Pasari and Dikshit 2014a), and the exponentiated exponential distribution (Pasari and Dikshit 2014c;Pasari 2015) has also been explored for identification of the most suitable probability model for a given earthquake catalog.…”

Section: Rationale and Previous Workmentioning

confidence: 99%

Earthquake interevent time distribution in Kachchh, Northwestern India

Dikshit

2015

Earth Planet Sp

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Statistical properties of earthquake interevent times have long been the topic of interest to seismologists and earthquake professionals, mainly for hazard-related concerns. In this paper, we present a comprehensive study on the temporal statistics of earthquake interoccurrence times of the seismically active Kachchh peninsula (western India) from thirteen probability distributions. Those distributions are exponential, gamma, lognormal, Weibull, Levy, Maxwell, Pareto, Rayleigh, inverse Gaussian (Brownian passage time), inverse Weibull (Frechet), exponentiated exponential, exponentiated Rayleigh (Burr type X), and exponentiated Weibull distributions. Statistical inferences of the scale and shape parameters of these distributions are discussed from the maximum likelihood estimations and the Fisher information matrices. The latter are used as a surrogate tool to appraise the parametric uncertainty in the estimation process. The results were found on the basis of two goodness-of-fit tests: the maximum likelihood criterion with its modification to Akaike information criterion (AIC) and the Kolmogorov-Smirnov (K-S) minimum distance criterion. These results reveal that (i) the exponential model provides the best fit, (ii) the gamma, lognormal, Weibull, inverse Gaussian, exponentiated exponential, exponentiated Rayleigh, and exponentiated Weibull models provide an intermediate fit, and (iii) the rest, namely Levy, Maxwell, Pareto, Rayleigh, and inverse Weibull, fit poorly to the earthquake catalog of Kachchh and its adjacent regions. This study also analyzes the present-day seismicity in terms of the estimated recurrence interval and conditional probability curves (hazard curves). The estimated cumulative probability and the conditional probability of a magnitude 5.0 or higher event reach 0.8-0.9 by 2027-2036 and 2034-2043, respectively. These values have significant implications in a variety of practical applications including earthquake insurance, seismic zonation, location identification of lifeline structures, and revision of building codes.

“…Yet, often times, many damaging earthquakes occur in unexpected places, where neither records of previous large earthquakes exist, nor any major geological faults have been identified so far. In such situations where geodetic or geophysical observations are notoriously limited to yield much information on current and future hazard of a region to the driven complex system of earthquakes, statistical analysis of classifying seismicity and related empirical hazard estimation can prove to be effective (Jordan, 2006;Pasari and Dikshit, 2015).…”

Section: Introductionmentioning

confidence: 99%

Nowcasting Earthquakes in the Northwest Himalaya and Surrounding Regions

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Mehta

2018

Abstract. With the rapid increase and availability of seismic data, an automatic, transparent and regular way of earthquake hazard estimation strategy is highly desirable in many seismically active large geographical regions. In this paper, we implement a novel method of nowcasting (Rundle et al., 2016) that can indirectly assess the current progression of a region through its earthquake cycle of large events. Nowcasting differs from the method of forecasting in which future earthquake probabilities are calculated. Using statistics of natural times, counts of small earthquakes between large earthquakes in a defined region, nowcasting provides an earthquake potential score (EPS) to enable scientists and city planners a snapshot of the current level of earthquake hazard in the region. Applied to a number of selected major cities in the northwest Himalaya and surrounding regions, we found that the EPS values corresponding to M&geq;6 events in New Delhi, Chandigarh, Dehradun and Shimla reach about 0.56, 0.87, 0.85 and 0.88, respectively. These estimated scores thus indicate that New Delhi is about half-way through its cycle for magnitude 6.0 or higher earthquakes, while Dehradun is about 85 percent of the way through its cycle. Towards the end, we discuss some implications and applications of these nowcast values to improve the present earthquake hazard assessment practice in the study region.